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Land Masking Methods of Sentinel-1 SAR Imagery for Ship Detection Considering Coastline Changes and Noise

  • Bae, Jeongju (Maritime Safety Research Center, Korea Institute of Ocean Science and Technology) ;
  • Yang, Chan-Su (Maritime Safety Research Center, Korea Institute of Ocean Science and Technology)
  • Received : 2016.12.29
  • Accepted : 2017.08.22
  • Published : 2017.08.31

Abstract

Since land pixels often generate false alarms in ship detection using Synthetic Aperture Radar (SAR), land masking is a necessary step which can be processed by a land area map or water database. However, due to the continuous coastline changes caused by newport, bridge, etc., an updated data should be considered to mask either the land or the oceanic part of SAR. Furthermore, coastal concrete facilities make noise signals, mainly caused by side lobe effect. In this paper, we propose two methods. One is a semi-automatic water body data generation method that consists of terrain correction, thresholding, and median filter. Another is a dynamic land masking method based on water database. Based on water database, it uses a breadth-first search algorithm to find and mask noise signals from coastal concrete facilities. We verified our methods using Sentinel-1 SAR data. The result shows that proposed methods remove maximum 84.42% of false alarms.

Keywords

References

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Cited by

  1. Automatic Ship Detection Using the Artificial Neural Network and Support Vector Machine from X-Band Sar Satellite Images vol.10, pp.11, 2018, https://doi.org/10.3390/rs10111799